article · Healthcare Analytics
Intracranial hemorrhage is a critical form of stroke caused by ruptured arteries bleeding into brain tissue. To classify computed tomography scans of this condition, an ensemble machine learning strategy employs a joint feature extraction mechanism. This approach merges texture characteristics from grey level co-occurrence matrices with transform features from discrete wavelet and discrete cosine transforms. Training data is balanced using the synthetic minority over-sampling technique, and a sequential forward feature selection process determines the most effective feature subsets. Performance assessments based on confusion matrices, precision, and recall confirm that ensemble classifiers deliver strong diagnostic accuracy. Utilizing a reduced set of six crucial features, a Random Forest classifier attained the highest overall accuracy of 87.22%.
Intracranial hemorrhage is a life-threatening form of stroke where swift and accurate diagnosis is critical. Developing automated classification models that identify brain bleeding from computed tomography scans using compact feature sets helps pave the way for more efficient, computer-assisted radiological analysis in emergency settings.
This research points toward potential applications in computer-aided diagnostic and decision-support software for radiologists analysing brain scans. Given that the abstract demonstrates an algorithm tested on training datasets with an 87.22% accuracy rate, the technology is at an early, applied research stage and remains distant from operational clinical deployment.
AI-generated from the published abstract. Always read the original work before citing.
One of the most serious forms of brain stroke is intracranial hemorrhage (ICH). When an artery bursts, the brain and the tissue around the artery start bleeding. This study proposes a joint feature selection strategy to classify computed tomography (CT) images of intracranial hemorrhage. The joint feature set is composed of transform and texture features. Joint features are constructed from a combination of grey level co-occurrence matrix (GLCM) features, discrete wavelet features (DWT), and discrete cosine features (DCT). Brain hemorrhage CT image classification uses ensemble-based machine learning (ML) techniques. On the training dataset, a Synthetic Minority Over-Sampling Technique (SMOTE) is applied to treat the problem of oversampling by adding fresh data. Additionally, the sequential forward feature selection technique is used to obtain feature subsets. The classification accuracy is further examined for varied feature vector sizes. Confusion matrix, precision, and recall in categorization are employed as performance evaluation measurements. The ML-based ensemble classifiers can produce highly accurate results with the aid of the proposed novel feature extraction mechanism. When taking into consideration a crucial feature set consisting of six features, it can be seen that Random Forest obtained the greatest accuracy, which is 87.22%.
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DOI: 10.1016/j.health.2023.100196
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